Overview

Dataset statistics

 TrainTest
Number of variables77
Number of observations536264
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows101
Duplicate rows (%)1.9%0.4%
Total size in memory33.5 KiB16.5 KiB
Average record size in memory64.0 B64.0 B

Variable types

 TrainTest
Numeric77

Alerts

TrainTest
Dataset has 10 (1.9%) duplicate rows Dataset has 1 (0.4%) duplicate rowsDuplicates
Total is highly overall correlated with HP and 5 other fieldsTotal is highly overall correlated with HP and 5 other fieldsHigh Correlation
HP is highly overall correlated with Total and 3 other fieldsHP is highly overall correlated with Total and 1 other fieldsHigh Correlation
Attack is highly overall correlated with Total and 2 other fieldsAttack is highly overall correlated with Total and 1 other fieldsHigh Correlation
Defense is highly overall correlated with Total and 2 other fieldsDefense is highly overall correlated with Total and 1 other fieldsHigh Correlation
Sp. Atk is highly overall correlated with Total and 2 other fieldsSp. Atk is highly overall correlated with Total and 1 other fieldsHigh Correlation
Sp. Def is highly overall correlated with Total and 3 other fieldsSp. Def is highly overall correlated with Total and 2 other fieldsHigh Correlation
Speed is highly overall correlated with TotalSpeed is highly overall correlated with TotalHigh Correlation

Reproduction

 TrainTest
Analysis started2023-11-17 13:13:02.3327042023-11-17 13:13:12.042273
Analysis finished2023-11-17 13:13:12.0366882023-11-17 13:13:21.019593
Duration9.7 seconds8.98 seconds
Software versionydata-profiling vv4.6.1ydata-profiling vv4.6.1
Download configurationconfig.jsonconfig.json

Variables

Total
Real number (ℝ)

 TrainTest
Distinct166120
Distinct (%)31.0%45.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean433.34328438.67424
 TrainTest
Minimum180194
Maximum780780
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size8.4 KiB4.1 KiB
2023-11-17T08:13:21.319597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum180194
5-th percentile248.75254.05
Q1325335
median440462
Q3515518.5
95-th percentile630618
Maximum780780
Range600586
Interquartile range (IQR)190183.5

Descriptive statistics

 TrainTest
Standard deviation121.12103117.72356
Coefficient of variation (CV)0.279503660.26836214
Kurtosis-0.59638969-0.28864589
Mean433.34328438.67424
Median Absolute Deviation (MAD)91.572
Skewness0.168302660.12407055
Sum232272115810
Variance14670.30413858.836
MonotonicityNot monotonicNot monotonic
2023-11-17T08:13:22.301863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 29
 
5.4%
580 18
 
3.4%
405 16
 
3.0%
300 15
 
2.8%
500 12
 
2.2%
480 10
 
1.9%
495 10
 
1.9%
490 10
 
1.9%
680 9
 
1.7%
310 9
 
1.7%
Other values (156) 398
74.3%
ValueCountFrequency (%)
500 11
 
4.2%
405 10
 
3.8%
600 8
 
3.0%
525 8
 
3.0%
490 8
 
3.0%
520 7
 
2.7%
330 6
 
2.3%
540 5
 
1.9%
580 5
 
1.9%
495 5
 
1.9%
Other values (110) 191
72.3%
ValueCountFrequency (%)
180 1
 
0.2%
190 1
 
0.2%
195 3
0.6%
198 1
 
0.2%
200 1
 
0.2%
205 5
0.9%
210 2
 
0.4%
215 1
 
0.2%
218 1
 
0.2%
220 3
0.6%
ValueCountFrequency (%)
194 1
 
0.4%
200 2
0.8%
210 1
 
0.4%
213 1
 
0.4%
220 1
 
0.4%
236 1
 
0.4%
240 1
 
0.4%
245 1
 
0.4%
250 4
1.5%
253 1
 
0.4%
ValueCountFrequency (%)
194 1
 
0.2%
200 2
0.4%
210 1
 
0.2%
213 1
 
0.2%
220 1
 
0.2%
236 1
 
0.2%
240 1
 
0.2%
245 1
 
0.2%
250 4
0.7%
253 1
 
0.2%
ValueCountFrequency (%)
180 1
 
0.4%
190 1
 
0.4%
195 3
1.1%
198 1
 
0.4%
200 1
 
0.4%
205 5
1.9%
210 2
 
0.8%
215 1
 
0.4%
218 1
 
0.4%
220 3
1.1%

HP
Real number (ℝ)

 TrainTest
Distinct7962
Distinct (%)14.7%23.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean69.20335869.371212
 TrainTest
Minimum201
Maximum255170
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size8.4 KiB4.1 KiB
2023-11-17T08:13:22.681445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum201
5-th percentile37.535.3
Q15050
median6665
Q38080.25
95-th percentile108.25110
Maximum255170
Range235169
Interquartile range (IQR)3030.25

Descriptive statistics

 TrainTest
Standard deviation25.61084925.42742
Coefficient of variation (CV)0.370081010.36654138
Kurtosis9.80488981.9636548
Mean69.20335869.371212
Median Absolute Deviation (MAD)1615
Skewness1.88986840.90631143
Sum3709318314
Variance655.91558646.55369
MonotonicityNot monotonicNot monotonic
2023-11-17T08:13:23.089749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 43
 
8.0%
50 40
 
7.5%
70 38
 
7.1%
80 31
 
5.8%
45 30
 
5.6%
75 28
 
5.2%
55 27
 
5.0%
40 25
 
4.7%
65 25
 
4.7%
90 21
 
3.9%
Other values (69) 228
42.5%
ValueCountFrequency (%)
60 24
 
9.1%
50 23
 
8.7%
65 21
 
8.0%
70 19
 
7.2%
75 15
 
5.7%
40 13
 
4.9%
80 12
 
4.5%
100 11
 
4.2%
55 10
 
3.8%
95 9
 
3.4%
Other values (52) 107
40.5%
ValueCountFrequency (%)
20 3
 
0.6%
25 2
 
0.4%
28 1
 
0.2%
30 9
 
1.7%
35 11
2.1%
36 1
 
0.2%
38 5
 
0.9%
39 2
 
0.4%
40 25
4.7%
41 1
 
0.2%
ValueCountFrequency (%)
1 1
 
0.4%
10 1
 
0.4%
20 3
 
1.1%
30 4
 
1.5%
31 1
 
0.4%
35 4
 
1.5%
37 1
 
0.4%
38 1
 
0.4%
40 13
4.9%
41 2
 
0.8%
ValueCountFrequency (%)
1 1
 
0.2%
10 1
 
0.2%
20 3
 
0.6%
30 4
 
0.7%
31 1
 
0.2%
35 4
 
0.7%
37 1
 
0.2%
38 1
 
0.2%
40 13
2.4%
41 2
 
0.4%
ValueCountFrequency (%)
20 3
 
1.1%
25 2
 
0.8%
28 1
 
0.4%
30 9
 
3.4%
35 11
4.2%
36 1
 
0.4%
38 5
 
1.9%
39 2
 
0.8%
40 25
9.5%
41 1
 
0.4%

Attack
Real number (ℝ)

 TrainTest
Distinct10067
Distinct (%)18.7%25.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean79.06343378.875
 TrainTest
Minimum510
Maximum190180
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size8.4 KiB4.1 KiB
2023-11-17T08:13:23.456009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum510
5-th percentile3030.75
Q15555
median7575
Q3100100
95-th percentile132.5146.7
Maximum190180
Range185170
Interquartile range (IQR)4545

Descriptive statistics

 TrainTest
Standard deviation32.33833532.758993
Coefficient of variation (CV)0.40901760.41532796
Kurtosis0.255754170.027835335
Mean79.06343378.875
Median Absolute Deviation (MAD)2021
Skewness0.525182860.6068723
Sum4237820823
Variance1045.76791073.1516
MonotonicityNot monotonicNot monotonic
2023-11-17T08:13:23.843585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 25
 
4.7%
100 24
 
4.5%
50 23
 
4.3%
85 23
 
4.3%
75 21
 
3.9%
70 21
 
3.9%
60 21
 
3.9%
90 20
 
3.7%
65 20
 
3.7%
55 20
 
3.7%
Other values (90) 318
59.3%
ValueCountFrequency (%)
65 19
 
7.2%
100 16
 
6.1%
50 14
 
5.3%
45 13
 
4.9%
60 12
 
4.5%
80 12
 
4.5%
75 11
 
4.2%
55 10
 
3.8%
70 10
 
3.8%
90 10
 
3.8%
Other values (57) 137
51.9%
ValueCountFrequency (%)
5 2
 
0.4%
10 2
 
0.4%
20 6
 
1.1%
24 1
 
0.2%
25 4
 
0.7%
27 1
 
0.2%
29 1
 
0.2%
30 15
2.8%
33 1
 
0.2%
35 11
2.1%
ValueCountFrequency (%)
10 1
 
0.4%
15 1
 
0.4%
20 2
 
0.8%
22 1
 
0.4%
23 1
 
0.4%
25 3
 
1.1%
30 5
1.9%
35 2
 
0.8%
38 1
 
0.4%
40 9
3.4%
ValueCountFrequency (%)
10 1
 
0.2%
15 1
 
0.2%
20 2
 
0.4%
22 1
 
0.2%
23 1
 
0.2%
25 3
 
0.6%
30 5
0.9%
35 2
 
0.4%
38 1
 
0.2%
40 9
1.7%
ValueCountFrequency (%)
5 2
 
0.8%
10 2
 
0.8%
20 6
 
2.3%
24 1
 
0.4%
25 4
 
1.5%
27 1
 
0.4%
29 1
 
0.4%
30 15
5.7%
33 1
 
0.4%
35 11
4.2%

Defense
Real number (ℝ)

 TrainTest
Distinct9066
Distinct (%)16.8%25.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean73.21268775.121212
 TrainTest
Minimum520
Maximum230230
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size8.4 KiB4.1 KiB
2023-11-17T08:13:24.223536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum520
5-th percentile3435.3
Q15050
median7070
Q39091
95-th percentile125130
Maximum230230
Range225210
Interquartile range (IQR)4041

Descriptive statistics

 TrainTest
Standard deviation31.41229530.733128
Coefficient of variation (CV)0.429055350.40911385
Kurtosis3.03541292.1440918
Mean73.21268775.121212
Median Absolute Deviation (MAD)2020
Skewness1.18552631.1078873
Sum3924219832
Variance986.73225944.52518
MonotonicityNot monotonicNot monotonic
2023-11-17T08:13:24.604649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 32
 
6.0%
80 30
 
5.6%
60 30
 
5.6%
85 26
 
4.9%
40 25
 
4.7%
45 24
 
4.5%
50 24
 
4.5%
100 24
 
4.5%
90 23
 
4.3%
65 22
 
4.1%
Other values (80) 276
51.5%
ValueCountFrequency (%)
50 25
 
9.5%
70 22
 
8.3%
60 16
 
6.1%
65 14
 
5.3%
90 12
 
4.5%
55 11
 
4.2%
40 11
 
4.2%
75 9
 
3.4%
100 9
 
3.4%
80 9
 
3.4%
Other values (56) 126
47.7%
ValueCountFrequency (%)
5 2
 
0.4%
10 1
 
0.2%
15 4
 
0.7%
20 2
 
0.4%
23 1
 
0.2%
25 1
 
0.2%
28 1
 
0.2%
30 12
2.2%
32 2
 
0.4%
34 2
 
0.4%
ValueCountFrequency (%)
20 2
 
0.8%
25 1
 
0.4%
30 2
 
0.8%
33 1
 
0.4%
34 1
 
0.4%
35 7
2.7%
37 2
 
0.8%
40 11
4.2%
41 2
 
0.8%
42 2
 
0.8%
ValueCountFrequency (%)
20 2
 
0.4%
25 1
 
0.2%
30 2
 
0.4%
33 1
 
0.2%
34 1
 
0.2%
35 7
1.3%
37 2
 
0.4%
40 11
2.1%
41 2
 
0.4%
42 2
 
0.4%
ValueCountFrequency (%)
5 2
 
0.8%
10 1
 
0.4%
15 4
 
1.5%
20 2
 
0.8%
23 1
 
0.4%
25 1
 
0.4%
28 1
 
0.4%
30 12
4.5%
32 2
 
0.8%
34 2
 
0.8%

Sp. Atk
Real number (ℝ)

 TrainTest
Distinct9668
Distinct (%)17.9%25.8%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean72.35447873.765152
 TrainTest
Minimum1010
Maximum180194
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size8.4 KiB4.1 KiB
2023-11-17T08:13:24.949366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum1010
5-th percentile3030
Q148.7550
median6565
Q39595
95-th percentile130135
Maximum180194
Range170184
Interquartile range (IQR)46.2545

Descriptive statistics

 TrainTest
Standard deviation31.93388834.310007
Coefficient of variation (CV)0.44135330.46512488
Kurtosis0.0558111850.62078844
Mean72.35447873.765152
Median Absolute Deviation (MAD)2220
Skewness0.657966680.87820664
Sum3878219474
Variance1019.77321177.1766
MonotonicityNot monotonicNot monotonic
2023-11-17T08:13:25.314174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 36
 
6.7%
40 34
 
6.3%
65 28
 
5.2%
50 25
 
4.7%
35 24
 
4.5%
55 22
 
4.1%
95 20
 
3.7%
70 20
 
3.7%
45 17
 
3.2%
105 16
 
3.0%
Other values (86) 294
54.9%
ValueCountFrequency (%)
45 16
 
6.1%
65 16
 
6.1%
60 15
 
5.7%
40 15
 
5.7%
50 14
 
5.3%
85 13
 
4.9%
55 13
 
4.9%
100 13
 
4.9%
80 12
 
4.5%
70 10
 
3.8%
Other values (58) 127
48.1%
ValueCountFrequency (%)
10 1
 
0.2%
15 3
 
0.6%
20 6
 
1.1%
24 1
 
0.2%
25 8
1.5%
27 1
 
0.2%
29 1
 
0.2%
30 16
3.0%
31 1
 
0.2%
32 2
 
0.4%
ValueCountFrequency (%)
10 2
 
0.8%
15 1
 
0.4%
20 2
 
0.8%
23 1
 
0.4%
24 1
 
0.4%
25 3
 
1.1%
27 1
 
0.4%
30 8
3.0%
35 5
1.9%
36 1
 
0.4%
ValueCountFrequency (%)
10 2
 
0.4%
15 1
 
0.2%
20 2
 
0.4%
23 1
 
0.2%
24 1
 
0.2%
25 3
 
0.6%
27 1
 
0.2%
30 8
1.5%
35 5
0.9%
36 1
 
0.2%
ValueCountFrequency (%)
10 1
 
0.4%
15 3
 
1.1%
20 6
 
2.3%
24 1
 
0.4%
25 8
3.0%
27 1
 
0.4%
29 1
 
0.4%
30 16
6.1%
31 1
 
0.4%
32 2
 
0.8%

Sp. Def
Real number (ℝ)

 TrainTest
Distinct8560
Distinct (%)15.9%22.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean71.25186673.223485
 TrainTest
Minimum2020
Maximum230160
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size8.4 KiB4.1 KiB
2023-11-17T08:13:25.731736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum2020
5-th percentile3035
Q15050
median65.570
Q38990
95-th percentile120122.55
Maximum230160
Range210140
Interquartile range (IQR)3940

Descriptive statistics

 TrainTest
Standard deviation28.26295126.930541
Coefficient of variation (CV)0.396662610.36778557
Kurtosis2.34109380.0060226655
Mean71.25186673.223485
Median Absolute Deviation (MAD)17.520
Skewness0.990592170.55459966
Sum3819119331
Variance798.79439725.25405
MonotonicityNot monotonicNot monotonic
2023-11-17T08:13:26.114114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 34
 
6.3%
60 33
 
6.2%
65 33
 
6.2%
55 31
 
5.8%
50 30
 
5.6%
75 26
 
4.9%
70 23
 
4.3%
40 22
 
4.1%
45 22
 
4.1%
90 20
 
3.7%
Other values (75) 262
48.9%
ValueCountFrequency (%)
50 20
 
7.6%
80 18
 
6.8%
70 17
 
6.4%
90 16
 
6.1%
55 16
 
6.1%
75 14
 
5.3%
45 13
 
4.9%
85 12
 
4.5%
65 11
 
4.2%
95 10
 
3.8%
Other values (50) 117
44.3%
ValueCountFrequency (%)
20 5
 
0.9%
23 1
 
0.2%
25 8
1.5%
30 14
2.6%
31 1
 
0.2%
32 1
 
0.2%
33 1
 
0.2%
35 14
2.6%
36 1
 
0.2%
37 1
 
0.2%
ValueCountFrequency (%)
20 1
 
0.4%
25 3
 
1.1%
30 6
2.3%
34 1
 
0.4%
35 4
1.5%
37 2
 
0.8%
39 1
 
0.4%
40 8
3.0%
41 2
 
0.8%
42 1
 
0.4%
ValueCountFrequency (%)
20 1
 
0.2%
25 3
 
0.6%
30 6
1.1%
34 1
 
0.2%
35 4
0.7%
37 2
 
0.4%
39 1
 
0.2%
40 8
1.5%
41 2
 
0.4%
42 1
 
0.2%
ValueCountFrequency (%)
20 5
 
1.9%
23 1
 
0.4%
25 8
3.0%
30 14
5.3%
31 1
 
0.4%
32 1
 
0.4%
33 1
 
0.4%
35 14
5.3%
36 1
 
0.4%
37 1
 
0.4%

Speed
Real number (ℝ)

 TrainTest
Distinct9870
Distinct (%)18.3%26.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean68.25746368.318182
 TrainTest
Minimum55
Maximum160180
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size8.4 KiB4.1 KiB
2023-11-17T08:13:26.486031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum55
5-th percentile2525
Q14545
median6565
Q39090
95-th percentile115110
Maximum160180
Range155175
Interquartile range (IQR)4545

Descriptive statistics

 TrainTest
Standard deviation29.12706528.979951
Coefficient of variation (CV)0.426723530.42419089
Kurtosis-0.392816470.11051886
Mean68.25746368.318182
Median Absolute Deviation (MAD)2021
Skewness0.364739030.34592384
Sum3658618036
Variance848.38593839.83754
MonotonicityNot monotonicNot monotonic
2023-11-17T08:13:26.885627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 33
 
6.2%
60 31
 
5.8%
70 29
 
5.4%
30 23
 
4.3%
55 22
 
4.1%
100 22
 
4.1%
65 20
 
3.7%
45 20
 
3.7%
80 17
 
3.2%
90 17
 
3.2%
Other values (88) 302
56.3%
ValueCountFrequency (%)
40 16
 
6.1%
65 16
 
6.1%
80 16
 
6.1%
90 14
 
5.3%
60 13
 
4.9%
50 13
 
4.9%
30 12
 
4.5%
85 11
 
4.2%
95 11
 
4.2%
45 9
 
3.4%
Other values (60) 133
50.4%
ValueCountFrequency (%)
5 1
 
0.2%
10 1
 
0.2%
15 8
 
1.5%
20 10
1.9%
23 2
 
0.4%
25 6
 
1.1%
28 4
 
0.7%
29 2
 
0.4%
30 23
4.3%
32 4
 
0.7%
ValueCountFrequency (%)
5 1
 
0.4%
10 2
 
0.8%
15 1
 
0.4%
20 5
1.9%
22 1
 
0.4%
23 2
 
0.8%
24 1
 
0.4%
25 4
 
1.5%
29 1
 
0.4%
30 12
4.5%
ValueCountFrequency (%)
5 1
 
0.2%
10 2
 
0.4%
15 1
 
0.2%
20 5
0.9%
22 1
 
0.2%
23 2
 
0.4%
24 1
 
0.2%
25 4
 
0.7%
29 1
 
0.2%
30 12
2.2%
ValueCountFrequency (%)
5 1
 
0.4%
10 1
 
0.4%
15 8
 
3.0%
20 10
3.8%
23 2
 
0.8%
25 6
 
2.3%
28 4
 
1.5%
29 2
 
0.8%
30 23
8.7%
32 4
 
1.5%

Interactions

Train

2023-11-17T08:13:10.203033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:19.185171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:02.461082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:12.166120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:03.973318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:13.341468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:05.167569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:14.529480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:06.397925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:15.698934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:07.631175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:16.779679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:08.996120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:17.966001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:10.446642image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:19.351988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:02.697911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:12.346432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:04.134867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:13.508116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:05.345306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:14.691521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:06.581939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:15.852147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:07.820785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:16.948367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:09.202143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:18.131424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:10.689386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:19.554723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:02.859645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:12.550330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:04.286927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:13.677018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:05.530132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:14.855173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:06.735688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:16.002085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:07.977820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:17.112989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:09.384264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:18.309104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:10.878838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:19.755528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:03.050453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:12.719029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:04.459156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:13.845072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:05.708850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:15.028973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:06.906112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:16.153133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:08.180326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:17.274202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:09.569209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:18.488535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:11.056818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:19.913670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:03.239123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:12.854755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:04.653709image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:13.990937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:05.871417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:15.173193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:07.046878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:16.283713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:08.348489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:17.414872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:09.713275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:18.649160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:11.248366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:20.113512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:03.440989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:13.015493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:04.837309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:14.159997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:06.043000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:15.340696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:07.242794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:16.452909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:08.558898image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:17.607440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:09.877115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:18.838617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:11.432051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:20.304331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:03.720290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:13.186867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:04.987111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:14.342254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:06.209327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:15.534187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:07.389573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:16.625239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:08.753321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:17.782524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

2023-11-17T08:13:10.020068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:19.014200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

Train

2023-11-17T08:13:27.130458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Test

2023-11-17T08:13:27.360622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Train

TotalHPAttackDefenseSp. AtkSp. DefSpeed
Total1.0000.7470.7190.7060.7270.7640.590
HP0.7471.0000.5830.4620.5040.5460.313
Attack0.7190.5831.0000.5270.3620.3290.419
Defense0.7060.4620.5271.0000.3480.5880.143
Sp. Atk0.7270.5040.3620.3481.0000.5670.455
Sp. Def0.7640.5460.3290.5880.5671.0000.325
Speed0.5900.3130.4190.1430.4550.3251.000

Test

TotalHPAttackDefenseSp. AtkSp. DefSpeed
Total1.0000.6420.7250.6260.7310.7360.524
HP0.6421.0000.5320.3770.4030.3840.165
Attack0.7250.5321.0000.4930.3650.3070.286
Defense0.6260.3770.4931.0000.2470.555-0.011
Sp. Atk0.7310.4030.3650.2471.0000.5800.467
Sp. Def0.7360.3840.3070.5550.5801.0000.310
Speed0.5240.1650.286-0.0110.4670.3101.000

Missing values

Train

2023-11-17T08:13:11.731187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.

Test

2023-11-17T08:13:20.618485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.

Train

2023-11-17T08:13:11.948888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Test

2023-11-17T08:13:20.893984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Train

TotalHPAttackDefenseSp. AtkSp. DefSpeed
501330405090305565
334280304055405560
285420709070606070
18395659040458075
58439070105105504020
7483254580100353728
83410508555656590
61305408035354570
462244303042304270
291205503555252515

Test

TotalHPAttackDefenseSp. AtkSp. DefSpeed
69660092105901259098
6674857575751259540
63350557045705060
533520506510710510786
66385656565505090
62148865901154511558
3464671007383738355
49048550921089210835
760320506060606030
45649560521684713830

Train

TotalHPAttackDefenseSp. AtkSp. DefSpeed
46649585105558550115
121450250553510550
614315709045154550
20251404540353556
70058091129907290108
7159055506517595150
1063253010590252550
2706801061309011015490
435309445844584461
10260060658017095130

Test

TotalHPAttackDefenseSp. AtkSp. DefSpeed
5934058510585405040
33253070110180606050
24253305635253572
780335496670445551
3515001709045904560
356330602535708060
51285357055455525
3314306090140505040
733213452260273029
629329505062406265

Duplicate rows

Train

TotalHPAttackDefenseSp. AtkSp. DefSpeed# duplicates
86001001001001001001005
549875986398631013
02055035552525152
13093952436050652
23165053485348642
34055864588065802
44808080808080802
65205065107105107862
75807911570125801112
96801261319513198992

Test

TotalHPAttackDefenseSp. AtkSp. DefSpeed# duplicates
05205065107105107863